Machine Learning Engineer

About the Role

Key Responsibilities

• Build, train, and optimize machine learning models for classification, regression, NLP, computer vision, or recommendation systems
• Deploy models into production environments using MLOps best practices
• Collaborate with data engineering and DevOps teams to ensure scalable data pipelines and model deployment
• Conduct model evaluation, tuning, and performance monitoring to ensure accuracy, reliability, and fairness
• Work with large-scale datasets using distributed computing frameworks like Spark or Dask
• Implement and maintain CI/CD pipelines for ML workflows
• Keep up with the latest research and tools in AI/ML and recommend improvements or new technologies
• Document processes, model decisions, and workflows for reproducibility and transparency

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field
• 2+ years of experience in a Machine Learning Engineer or similar role
• Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost
• Experience deploying models using Docker, Kubernetes, MLflow, or similar tools
• Strong understanding of machine learning algorithms, statistics, and data preprocessing techniques
• Familiarity with cloud platforms (AWS SageMaker, Google Vertex AI, or Azure ML)
• Experience working with version control systems (e.g., Git) and modern DevOps pipelines
• Strong communication and collaboration skills

Nice to Have

• Experience with LLMs, transformer-based architectures, or fine-tuning large-scale pre-trained models
• Knowledge of MLOps frameworks and orchestration tools (e.g., Kubeflow, Airflow)
• Familiarity with big data tools (e.g., Spark, Hadoop)
• Understanding of data privacy, security, and model interpretability